Theta-tACS normalizes brain network activity in patients with traumatic brain injury

2019 ◽  
Vol 12 (2) ◽  
pp. 498
Author(s):  
I. Violante ◽  
L. Li ◽  
D. Sharp
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregory Simchick ◽  
Kelly M. Scheulin ◽  
Wenwu Sun ◽  
Sydney E. Sneed ◽  
Madison M. Fagan ◽  
...  

AbstractFunctional magnetic resonance imaging (fMRI) has significant potential to evaluate changes in brain network activity after traumatic brain injury (TBI) and enable early prognosis of potential functional (e.g., motor, cognitive, behavior) deficits. In this study, resting-state and task-based fMRI (rs- and tb-fMRI) were utilized to examine network changes in a pediatric porcine TBI model that has increased predictive potential in the development of novel therapies. rs- and tb-fMRI were performed one day post-TBI in piglets. Activation maps were generated using group independent component analysis (ICA) and sparse dictionary learning (sDL). Activation maps were compared to pig reference functional connectivity atlases and evaluated using Pearson spatial correlation coefficients and mean ratios. Nonparametric permutation analyses were used to determine significantly different activation areas between the TBI and healthy control groups. Significantly lower Pearson values and mean ratios were observed in the visual, executive control, and sensorimotor networks for TBI piglets compared to controls. Significant differences were also observed within several specific individual anatomical structures within each network. In conclusion, both rs- and tb-fMRI demonstrate the ability to detect functional connectivity disruptions in a translational TBI piglet model, and these disruptions can be traced to specific affected anatomical structures.


2008 ◽  
Vol 39 (01) ◽  
Author(s):  
F Otto ◽  
J Opatz ◽  
R Hartmann ◽  
D Willbold ◽  
E Donauer ◽  
...  

Brain Injury ◽  
2011 ◽  
Vol 25 (12) ◽  
pp. 1170-1187 ◽  
Author(s):  
Maki Kasahara ◽  
David K. Menon ◽  
Claire H. Salmond ◽  
Joanne G. Outtrim ◽  
Joana V. Taylor Tavares ◽  
...  

2014 ◽  
Vol 25 (8) ◽  
pp. 2306-2320 ◽  
Author(s):  
David Cantu ◽  
Kendall Walker ◽  
Lauren Andresen ◽  
Amaro Taylor-Weiner ◽  
David Hampton ◽  
...  

2017 ◽  
Vol 1 (2) ◽  
pp. 116-142 ◽  
Author(s):  
Ibai Diez ◽  
David Drijkoningen ◽  
Sebastiano Stramaglia ◽  
Paolo Bonifazi ◽  
Daniele Marinazzo ◽  
...  

Traumatic brain injury (TBI) affects structural connectivity, triggering the reorganization of structural–functional circuits in a manner that remains poorly understood. We focus here on brain network reorganization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severe TBI, comparing them to young, typically developing control participants. TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: (1) a subcortical network, including parts of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulate gyrus, and precuneus; and (2) a task-positive network, involving regions of the dorsal attention system, together with dorsolateral and ventrolateral prefrontal regions. We also found that the increased prefrontal connectivity in TBI patients was correlated with some postural control indices, such as the amount of body sway, whereby patients with worse balance increased their connectivity in frontal regions more strongly. The increased prefrontal connectivity found in TBI patients may provide the structural scaffolding for stronger cognitive control of certain behavioral functions, consistent with the observations that various motor tasks are performed less automatically following TBI and that more cognitive control is associated with such actions.


2021 ◽  
Author(s):  
Shan H. Siddiqi ◽  
Sridhar Kandala ◽  
Carl D. Hacker ◽  
Nicholas T. Trapp ◽  
Eric C. Leuthardt ◽  
...  

Abstract Background At the group level, antidepressant efficacy of rTMS targets is inversely related to their normative connectivity with subgenual anterior cingulate cortex (sgACC). Individualized connectivity may yield better targets, particularly in patients with neuropsychiatric disorders who may have aberrant connectivity. However, sgACC connectivity shows poor test-retest reliability at the individual level. Individualized resting-state network mapping (RSNM) can reliably map inter-individual variability in brain network organization. Objective To identify individualized RSNM-based rTMS targets that reliably target the sgACC connectivity profile. Methods We used RSNM to identify network-based rTMS targets in 10 healthy controls and 13 individuals with traumatic brain injury-associated depression (TBI-D). These “RSNM targets” were compared with consensus structural targets and targets based on individualized anti-correlation with a group-mean-derived sgACC region (“anti-group-mean sgACC targets”). The TBI-D cohort was randomized to receive active (n=9) or sham (n=4) rTMS to RSNM targets. Results The group-mean sgACC connectivity profile was reliably estimated by individualized correlation with default mode network (DMN) and anti-correlation with dorsal attention network (DAN). Individualized RSNM targets were then identified based on DAN anti-correlation and DMN correlation. Counterintuitively, anti-correlation with the group-mean sgACC connectivity profile was stronger and more reliable for RSNM-derived targets than for “anti-group-mean sgACC targets”. Improvement in depression after RSNM-targeted rTMS was predicted by target anti-correlation with the portions of sgACC. Active treatment led to increased connectivity within and between several relevant regions. Conclusions RSNM may enable reliable individualized rTMS targeting, although further research is needed to determine whether this personalized approach can improve clinical outcomes.


2021 ◽  
Author(s):  
Yusuf Osmanlioglu ◽  
Drew Parker ◽  
Jacob A Alappatt ◽  
James J Gugger ◽  
Ramon R Diaz-Arrastia ◽  
...  

Traumatic brain injury (TBI) is a major public health problem. Caused by external mechanical forces, a major characteristic of TBI is the shearing of axons across the white matter, which causes structural connectivity disruptions between brain regions. This diffuse injury leads to cognitive deficits, frequently requiring rehabilitation. Heterogeneity is another characteristic of TBI as severity and cognitive sequelae of the disease have a wide variation across patients, posing a big challenge for treatment. Thus, measures assessing network-wide structural connectivity disruptions in TBI are necessary to quantify injury burden of individuals, which would help in achieving personalized treatment, patient monitoring, and rehabilitation planning. Despite TBI being a disconnectivity syndrome, connectomic assessment of structural disconnectivity has been very scarce. In this study, we propose a novel connectomic measure that we call network anomaly score (NAS) to capture the integrity of structural connectivity in TBI patients by leveraging two major characteristics of the disease: diffuseness of axonal injury and heterogeneity of the disease. Over a longitudinal cohort of moderate-to-severe TBI patients, we demonstrate that structural network topology of patients are more heterogeneous and are significantly different than that of healthy controls at 3 months post-injury, where dissimilarity further increases up to 12 months. We also show that NAS captures injury burden as quantified by post-traumatic amnesia and that alterations in the structural brain network is not related to cognitive recovery. Finally we compare NAS to major graph theory measures used in TBI literature and demonstrate the superiority of NAS in characterizing the disease.


2009 ◽  
Vol 120 (1) ◽  
pp. e11
Author(s):  
F. Otto ◽  
J. Opatz ◽  
R. Hartmann ◽  
D. Willbold ◽  
E. Donauer ◽  
...  

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